I am trying to compute Silhouette with k-means. However I have the value really close to 0 and the clusters are very clearly separated. Do you know where can be the problem? This is the code:
n_samples, n_features = data.shape
n_digits=2
labels=data[:,-1]
data=data[:,:-1]
sample_size = 300
print("n_digits: %d, \t n_samples %d, \t n_features %d"
% (n_digits, n_samples, n_features))
print(79 * '_')
print('% 9s' % 'init'
' time inertia homo compl v-meas ARI AMI silhouette')
def bench_k_means(estimator, name, data):
t0 = time()
estimator.fit(data)
print('% 9s %.2fs %i %.3f %.3f %.3f %.3f %.3f %.3f'
% (name, (time() - t0), estimator.inertia_,
metrics.homogeneity_score(labels, estimator.labels_),
metrics.completeness_score(labels, estimator.labels_),
metrics.v_measure_score(labels, estimator.labels_),
metrics.adjusted_rand_score(labels, estimator.labels_),
metrics.adjusted_mutual_info_score(labels, estimator.labels_),
metrics.silhouette_score(data, estimator.labels_,
metric='euclidean',
sample_size=sample_size)))
pca = PCA(n_components=n_digits).fit(data)
bench_k_means(KMeans(init=pca.components_, n_clusters=n_digits, n_init=1),
name="PCA-based",
data=data)
print(79 * '_')
The obtained Silhouette is 0.052 and this is the obtained k-means clustering.
Thanks,
Laia
data = data[:,:-1]
. Then, show the result, and why you think it's good (and use the formatter to make it readable). This PCA based initialization is supposedly not meaningful - consider sticking to the default initialization. $\endgroup$